Image Synthesis with a Single (Robust) Classifier
arXiv:1906.09453
The paper demonstrates that a single off‑the‑shelf classifier, when trained to be adversarially robust, can be used for various image synthesis tasks by directly manipulating salient image features.
Abstract
We show that the basic classification framework alone can be used to tackle some of the most challenging tasks in image synthesis. In contrast to other state-of-the-art approaches, the toolkit we develop is rather minimal: it uses a single, off-the-shelf classifier for all these tasks. The crux of our approach is that we train this classifier to be adversarially robust. It turns out that adversarial robustness is precisely what we need to directly manipulate salient features of the input. Overall, our findings demonstrate the utility of robustness in the broader machine learning context. Code and models for our experiments can be found at https://git.io/robust-apps.